a. Field of Invention
The invention relates to structural monitoring and, more particularly, to a hardware and software architecture for dynamic modal structural monitoring and control that monitors the dynamics of a structure such as an aircraft in the presence of complex loading patterns using a robust modal filtering architecture, with integrated distributed shape and accelerometer sensing.
b. Background of the Invention
Monitoring the states of a structure is useful for safety, control and performance optimization in a wide variety of applications. For example, fuel efficiency and safety are of paramount importance to the aviation industry. Toward this end, lightweight composite material use in aircraft is now a common practice. However, increased flexibility leads to an aircraft which is more susceptible to dynamic phenomena such as gusts, flutter, and buffeting, which negatively affect safety and performance and can be potentially destructive. Consequently, demand has grown in the aviation industry for aircraft that are capable of monitoring and managing such dynamics. Typical aeroelastic control methods identify optimal control surface settings for minimum drag from indirect sensory data such as wing root load balances, engine RPM or thrust/fuel, but such parameters do not give an accurate reflection of the true structural state of an aircraft.
Several approaches have been proposed for more reliable real-time estimation of structural states. However most are frequency based methods which utilize conventional sensors to monitor a structure's natural frequency response. These methods are suitable for single-mode vibration suppression, but have limited effectiveness for the multi-mode vibration suppression. Flexible aircraft are highly complex structures with multiple vibration modes, and active control of such large flexible structures is more readily achieved with multiple input multiple output (MIMO) control algorithms.
An estimator which supports a MIMO type control algorithm is the spatial filter or modal filter. Modal filtering is a known spatial filtering technique which transforms physical coordinates to modal coordinates. The modal coordinates can be used to capture the contribution of both static and dynamic deformation in the structure. Modal filtering methods are generally well known in the art and are disclosed in numerous publications including Shelley, S. J., Investigation of Discrete Modal Filters for Structural Dynamics Applications, Department of Mechanical and Industrial Engineering, University of Cincinnati, 1990. Discrete/continuous modal filters and dynamic state estimators have typically been used for sensing modal coordinates in trusses and bridges.
A pseudo inverse modal filter, developed by Zhang, was implemented by Shelley et al. on a five meter truss structure, showing promise for real-time monitoring of the structure. Shelley, S., Freudinger, L, Allemang, R. J., and Zhang, Q. “Implementation of a Modal Filter on a Five Meter Truss Structure.” IMAC IX Conference, Florence Italy, Proceeding Volume 2. p. 1036-1044. Unfortunately the pseudo-inverse modal filter is not fault-tolerant and fails as soon as one sensor fails.
Shelley et al. later proposed an adaptive modal filter technique to compensate for small sensor failure or calibration drift. Shelley, S. J., Allemang, R. J., Slater, G. L., Shultze, J. F., Active Vibration Control Utilizing an Adaptive Modal Filter Based Modal Control Method, 11th International Modal Analysis Conference, Kissimmee, Fla., Feb. 1-4, 1993. However, the Shelley et al. adaptive modal filter must be programmed with the natural frequency, modal damping, and modal residue of the structure. While these parameters can be initially calculated, they will tend to drift over time. Shelly's algorithm is also based upon least mean squares (LMS), which has similarly bad resistance to gross outliers (large sensor bias) as least squares. In flight, the strain can be very high. If a sensor failure occurs and the sensor reading is near zero while the rest are at normal values, the sensor reading near zero is characterized as a gross outlier. This could completely bias a modal filter estimate using either least mean squares or ordinary least squares.
Nevertheless, if these past deficits can be overcome the modal filter has the potential to benefit the aerospace field significantly. What is needed is a system and method for dynamic modal structural monitoring and control that integrates a very large-scale array of sensors including distributed strain and acceleration sensing for the purposes of robust modal filtering (in lieu of the pseudo-inverse modal filter), in real time, while being completely tolerant to multiple sensor failures and asymmetric sensor error.
The present invention provides such a system, and in so doing offers more reliable estimates of the modal displacements and velocities for flexible aircraft structural feedback control than the current state of the art, especially in the presence of uncertainty and multiple sensor failures.